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Open AccessArticle
IoT-Based Assessment of a Driver’s Stress Level
by
Veronica Mattioli
Veronica Mattioli 1,2
,
Luca Davoli
Luca Davoli 2
,
Laura Belli
Laura Belli 2
,
Sara Gambetta
Sara Gambetta 3,
Luca Carnevali
Luca Carnevali 3
,
Andrea Sgoifo
Andrea Sgoifo 3
,
Riccardo Raheli
Riccardo Raheli
Riccardo Raheli is Professor of Communication Engineering at the University of Parma, Italy, which a [...]
Riccardo Raheli is Professor of Communication Engineering at the University of Parma, Italy, which he joined in 1991. From 1988 to 1991, he was with the Sant'Anna School of Advanced Studies, Pisa, Italy. From 1986 to 1988 he was with Siemens Telecommunications, Milan, Italy. In 1990 and 1993, he spent leaves as a Visiting Assistant Professor at the University of Southern California, Los Angeles, USA. His scientific interests are in the general area of systems for the communication, processing and storage of information, in which he has published diversely and extensively. He has served as an Editorial Board Member and Technical Program Committee Co-Chair of prestigious international journals and conferences.
1
and
Gianluigi Ferrari
Gianluigi Ferrari 2,*
1
Multimedia Laboratory, Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
2
Internet of Things (IoT) Laboratory, Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
3
Stress Physiology Laboratory, Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(17), 5479; https://doi.org/10.3390/s24175479 (registering DOI)
Submission received: 29 June 2024
/
Revised: 19 August 2024
/
Accepted: 22 August 2024
/
Published: 23 August 2024
Abstract
Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.
Share and Cite
MDPI and ACS Style
Mattioli, V.; Davoli, L.; Belli, L.; Gambetta, S.; Carnevali, L.; Sgoifo, A.; Raheli, R.; Ferrari, G.
IoT-Based Assessment of a Driver’s Stress Level. Sensors 2024, 24, 5479.
https://doi.org/10.3390/s24175479
AMA Style
Mattioli V, Davoli L, Belli L, Gambetta S, Carnevali L, Sgoifo A, Raheli R, Ferrari G.
IoT-Based Assessment of a Driver’s Stress Level. Sensors. 2024; 24(17):5479.
https://doi.org/10.3390/s24175479
Chicago/Turabian Style
Mattioli, Veronica, Luca Davoli, Laura Belli, Sara Gambetta, Luca Carnevali, Andrea Sgoifo, Riccardo Raheli, and Gianluigi Ferrari.
2024. "IoT-Based Assessment of a Driver’s Stress Level" Sensors 24, no. 17: 5479.
https://doi.org/10.3390/s24175479
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